盒内非相干运动
医学
淋巴结
磁共振弥散成像
淋巴
峰度
有效扩散系数
部分各向异性
结直肠癌
磁共振成像
核医学
放射科
病理
癌症
数学
统计
内科学
作者
Andrada Ianuș,Inês Santiago,António Galzerano,Paula Montesinos,Nuno Loução,Javier Sánchez‐González,Daniel C. Alexander,Celso Matos,Noam Shemesh
摘要
Purpose Mesorectal lymph node staging plays an important role in treatment decision making. Here, we explore the benefit of higher‐order diffusion MRI models accounting for non‐Gaussian diffusion effects to classify mesorectal lymph nodes both 1) ex vivo at ultrahigh field correlated with histology and 2) in vivo in a clinical scanner upon patient staging. Methods The preclinical investigation included 54 mesorectal lymph nodes, which were scanned at 16.4 T with an extensive diffusion MRI acquisition. Eight diffusion models were compared in terms of goodness of fit, lymph node classification ability, and histology correlation. In the clinical part of this study, 10 rectal cancer patients were scanned with diffusion MRI at 1.5 T, and 72 lymph nodes were analyzed with Apparent Diffusion Coefficient (ADC), Intravoxel Incoherent Motion (IVIM), Kurtosis, and IVIM‐Kurtosis. Results Compartment models including restricted and anisotropic diffusion improved the preclinical data fit, as well as the lymph node classification, compared to standard ADC. The comparison with histology revealed only moderate correlations, and the highest values were observed between diffusion anisotropy metrics and cell area fraction. In the clinical study, the diffusivity from IVIM‐Kurtosis was the only metric showing significant differences between benign (0.80 ± 0.30 μm 2 /ms) and malignant (1.02 ± 0.41 μm 2 /ms, P = .03) nodes. IVIM‐Kurtosis also yielded the largest area under the receiver operating characteristic curve (0.73) and significantly improved the node differentiation when added to the standard visual analysis by experts based on T 2 ‐weighted imaging. Conclusion Higher‐order diffusion MRI models perform better than standard ADC and may be of added value for mesorectal lymph node classification in rectal cancer patients.
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